Machine Learning: Unsupervised Learning

Online Course

Udacity

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What is the course about?
Prerequisites & Facts
Cost, Certificate & Degree
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What is the course about?

Machine Learning: Unsupervised Learning

The course Machine Learning: Unsupervised Learning is an online class provided by Udacity. The skill level of the course is Intermediate. It may be possible to receive a verified certification or use the course to prepare for a degree.

This is the second course in the 3-course Machine Learning Series and is offered at Georgia Tech as CS7641. Taking this class here does not earn Georgia Tech credit.

Course description

Machine Learning: Unsupervised Learning
1 months

Conversations on Analyzing Data
Ever wonder how Netflix can predict what movies you’ll like? Or how Amazon knows what you want to buy before you do? The answer can be found in Unsupervised Learning!
Closely related to pattern recognition, Unsupervised Learning is about analyzing data and looking for patterns. It is an extremely powerful tool for identifying structure in data. This course focuses on how you can use Unsupervised Learning approaches — including randomized optimization, clustering, and feature selection and transformation — to find structure in unlabeled data.
Series Information: Machine Learning is a graduate-level series of 3 courses, covering the area of Artificial Intelligence concerned with computer programs that modify and improve their performance through experiences.
The entire series is taught as an engaging dialogue between two eminent Machine Learning professors and friends: Professor Charles Isbell (Georgia Tech) and Professor Michael Littman (Brown University).

Randomized optimization
Clustering
Feature Selection
Feature Transformation
Information Theory
Unsupervised Learning Project

Optimization, randomized
Hill climbing
Random restart hill climbing
Simulated annealing
Annealing algorithm
Properties of simulated annealing
Genetic algorithms
GA skeleton
Crossover example
What have we learned
MIMIC
MIMIC: A probability model
MIMIC: Pseudo code
MIMIC: Estimating distributions
Finding dependency trees
Probability distribution
Clustering and expectation maximization
Basic clustering problem
Single linkage clustering (SLC)
Running time of SLC
Issues with SLC
K-means clustering

This class will assume that you have programming experience as you will be expected to work with python libraries such as numpy and scikit. A good grasp of probability and statistics is also required. Udacity’s Intro to Statistics, especially Lessons 8, 9 and 10, may be a useful refresher.
An introductory course like Udacity’s Introduction to Artificial Intelligence also provides a helpful background for this course.
See the Technology Requirements for using Udacity.

You will learn about and practice a variety of Unsupervised Learning approaches, including: randomized optimization, clustering, feature selection and transformation, and information theory.
You will learn important Machine Learning methods, techniques and best practices, and will gain experience implementing them in this course through a hands-on final project in which you will be designing a movie recommendation system (just like Netflix!).

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Prerequisites & Facts

Machine Learning: Unsupervised Learning

Course Topic

Computer Science, Programming

University, College, Institution

Udacity

Course Skill Level

Intermediate

see course summary

Course Language

English

Place of class

Online, self-paced (see curriculum for more information)

Degree

Certificate

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Degree & Cost

Machine Learning: Unsupervised Learning

To obtain a verified certificate from Udacity you have to finish this course or the latest version of it, if there is a new edition. The class may be free of charge, but there could be some cost to receive a verified certificate or to access the learning materials. The specifics of the course may have been changed, please consult the provider to get the latest quotes and news.

Udacity

Machine Learning: Unsupervised Learning

provided by Udacity

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